An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area.Downloads available: https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Administrative+Forest+Boundaries

The Oregon Department of Forestry's (ODF) GIS goal is to support the stewardship of Oregon's forests through the acquisition, analysis, distribution and display of geographic information. We are using ArcGIS Online as tool to help our state agency upload, collaborate, and expose geospatial data online. ODF was established in 1911. It is under the direction of the State Forester who is appointed by the State Board of Forestry. The statutes direct the state forester to act on all matters pertaining to forestry, including collecting and sharing information about the conditions of Oregon's forests, protecting forestlands and conserving forest resources.Our Agency tasks include: Fire protection for 16 million acres of private, state and federal forests.Regulation of forest practices (under the Oregon Forest Practices Act) and promotion of forest stewardship.The implementation of the Oregon Plan for Salmon and Watersheds.Detection and control of harmful forest insect pests and forest tree diseases on 12 million acres of state and private lands.Management of 818,800 acres of state-owned forestlands.Forestry assistance to Oregon's 166,000 non-industrial private woodland owners.Forest resource planning.Community and urban forestry assistance.Contact:Contact:Steve TimbrookGIS Data AdministratorAdministrative BranchInformation Technology Program - GIS UnitOregon Department of Forestrysteve.timbrook@odf.oregon.gov503.931.2755

Forest resources in Washington and Oregon were surveyed in the 1930s by employees of the USDA Forest Service, Pacific Northwest Forest Experiment Station. As part of this process, forest cover maps were created on paper at an original scale of 1:253,440. Forest and land cover types recorded include classifications such as: agricultural, balsam fir mountain hemlock, cedar-redwood, deforested burns, Douglas-fir, hardwood, juniper, lodgepole pine, non-forest pine mix, ponderosa pine, recent cutover, spruce-hemlock, subalpine and non-commercial, water, etc. An additional subcategory classification is also provided which gives additional insight into tree size classes for conifers or species group for hardwoods. These forest and land cover types are provided as both a shapefile and geopackage for Washington and Oregon combined.The 1928 McSweeney-McNary Forestry Research Act (P.L. 70-466, 45 Stat. 699-702) directed the Secretary of Agriculture to make and keep current a comprehensive inventory and analysis of the nation's forest resources. The decision was made to begin the nationwide survey with the Douglas-fir region and shortly thereafter to expand to the other forested lands of Washington and Oregon. Surveys were conducted between 1930 and 1936. Results of these surveys were reported in many formats including quarter state maps (4 maps per state) as well as many printed reports.The history of this project and copies of some of the early results as well, were published in Harrington (2003) which included a CD with a digital map (an ArcView GIS shapefile) for all of Washington and Oregon.

The Oregon Sand Dunes are the largest expanse of coastal sand dunes in North America. The Oregon Dunes National Recreation Area is located on the Oregon Coast, stretching approximately 40 miles north from the Coos River in North Bend, to the Siuslaw River, in Florence. The NRA is part of Siuslaw National Forest and is administered by the United States Forest Service. The Oregon Dunes are featured in Stories in Stone - Geologic Resources of Our National Forests and this map was created to support that project. The app allows you to explore the area using either aerial photography, LiDAR imagery or both.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This is a dataset download, not a document. The Open Document button will start the download.This data layer is an element of the Oregon GIS Framework. This data layer represents the Existing Vegetation data element. This statewide grid was created by combining four independently-generated datasets: one for western Oregon (USGS zones 2 and 7), and two for eastern Oregon (USGS zones 8 and 9; forested and non-forested lands), and selected wetland types from the Oregon Wetlands geodatabase. The landcover grid for zones 2 and 7 was produced using a modification of Breiman's Random Forest classifier to model landcover. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to build two predictive models for the forested landcover classes, and the nonforested landcover classes. The grids resulting from the models were then modified to improve the distribution of the following classes: volcanic systems and wetland vegetation. Along the eastern edge, the sagebrush systems were modified to help match with the map for the adjacent region. Additional classes were then layered on top of the modified models from other sources. These include disturbed classes (harvested and burned), cliffs, riparian, and NLCD's developed, agriculture, and water classes. A validation for forest classes was performed on a withheld of the sample data to assess model performance. Due to data limitations, the nonforest classes were evaluated using the same data that were used to build the original nonforest model. Two independent grids were combined to map landcover in adjacent zones 8 and 9. Tree canopy greater than 10% (from NLCD 2001), complemented with a disturbance grid, served as a mask to delineate forested areas. A grid of non-forested areas was extracted from a larger, regional grid (Sagemap) created using decision tree classifier and other techniques. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to derive rule sets for the various landcover classes. Eleven mapping areas, each characterized by similar ecological and spectral characteristics, were modeled independently of one another and mosaicked. An internal validation for modeled classes was performed on a withheld 20% of the sample data to assess model performance. The portion of this original grid corresponding to USGS map zones 8 and 9 was extracted and split into three mapping areas (one for USGS zone 8, two for USGS zone 9: Northern Basin and Range in the south, Blue Mountains in the north) and modified to improve the distribution of the following classes: cliffs, subalpine zone, dunes, lava flows, silver sagebrush, ash beds, playas, scabland, and riparian vegetation. Agriculture and urban areas were extracted from NLCD 2001. A forest grid was generated using Gradient Nearest Neighbor (GNN) imputation process. GNN uses multivariate gradient modeling to integrate data from regional grids of field plots with satellite imagery and mapped environmental data. A suite of fine-scale plot variables is imputed to each pixel in a digital map, and regional maps can be created for most of the same vegetation attributes available from the field plots. However, due to lack of sampling plots in the southern half of zone 9, the GNN model proved unreliable there; forest data from Landfire were used instead. To compensate for known under-representation of wetlands, selected wetland types from the Oregon Wetlands Geodatabase (version 2009-1030) were converted to raster and overlaid (replaced) pixel value assignments from the previous steps just detailed. See Process Steps for more information. The ecological systems were crosswalked to landcover (based on Oregon landcover standard, modified from NLCD 2001) and to wildlife habitats (based on integrated habitats used in the Oreg

Oregon Ownership and Admin Boundaries managed by Oregon Department of Forestry (2021).This includes Public Ownership, Counties, ODF Forest Protection Districts, and ODF Units, for the entire State of Oregon. This is only an export of the master data and is not updated on a regular schedule. Please see the source data to ensure accuracy and ensure it is up to date. Last updated on 7/11/2021, BRM..Useful Links:www.oregon.gov/odfhttps://www.oregon.gov/ODF/AboutODF/Pages/MapsData

Wilderness areas are federally-owned public lands managed by the federal government through four agencies, the Bureau of Land Management, Fish and Wildlife Service, Forest Service, and National Park Service. When the National Wilderness Preservation System started in 1964, only 54 wilderness areas were included. Since then, the system has grown nearly every year to include more than 800. The time component of this service is based on the year in which the wilderness was originally designated (additions may have occurred in subsequent years). Overall, however, only about 5% of the entire United States—an area slightly larger than the state of California— is protected as wilderness. Because Alaska contains just over half of America's wilderness, only about 2.7% of the contiguous United States—an area about the size of Minnesota—is protected as wilderness. To learn more about wilderness areas, visit Wilderness Connect, the authoritative source for wilderness information online. Wilderness Connect also publishes two other map resources:An interactive wilderness map allows visitors to search for and explore all wilderness areas in the United States. Fact-filled storymaps on the benefits of wilderness illustrate how wilderness protects values including clean water, wildlife habitat, nearby recreation, cultural sites and more.

Although wilderness areas are federally-owned, some areas contain non-federal parcels within their boundaries. Non-federal lands within some wilderness areas are included as part of this feature dataset as a separate layer. Termed inholdings or edgeholdings, these lands are privately-owned or owned by local governments, state governments or Indigenous Nations. Hundreds of inholdings and edgeholdings exist across the wilderness system. Generally, however, they are small compared to the size of the wilderness itself. Since the rules and regulations that apply to wilderness areas do not apply to these non-federally-owned parcels, it is important for wilderness visitors to know their location to avoid trespassing where access is not allowed. The owners of inholdings and edgeholdings can develop these parcels (as long as developments do not affect the character of the surrounding wilderness lands) and they retain special and limited access to them, sometimes, but not always, by motorized means.

This dataset consists of repeat vegetation cover maps of multiple Willamette River restoration sites where restoration activities were implemented to increase the area of floodplain forests. Beginning in the early 21st century, large-scale restoration programs have been implemented along the Willamette River, Oregon, to address historical losses of floodplain habitats for native fish (Keith and others, 2022). For much of the Willamette River floodplain, direct enhancement of floodplain habitats through restoration activities is needed because the underlying hydrologic, geomorphic, and vegetation processes that historically created and sustained complex floodplain habitats have been fundamentally altered by dam construction, bank protection, large wood removal, land conversion, and other influences (for example, Hulse and others, 2002; Wallick and others, 2013). Floodplain forest vegetation cover was derived from R Random Forest classification of 2009, 2011, 2018, and 2020 aerial imagery at three large-scale floodplain planting restoration sites along the Willamette River: Harkens Lake (river kilometer [RKM] 153-154.5), Snag Boat Bend (RKM 144-147), and Luckiamute State Natural Area (RKM 108-111). The overall goals and approaches for the repeat mapping are based on a previously published effectiveness monitoring framework for Willamette River restoration activities (Keith and others, 2022). The repeat mapping datasets include GIS layers defining two classes of vegetation cover (forest and not-forest, condensed from six cover classes: forest, not-forest (agriculture), not-forest (other), water, shadow in forest, and shadow in non-forested areas). This mapping can be used to support an assessment of changes to floodplain forest vegetation cover at sites along the Willamette River floodplain where restoration activities were implemented from 2012 to 2020 to increase the area of native floodplain forest vegetation.

FIRE1920_POLY: A series of four maps showing the state of forests in the northern coastal area of Oregon. They show the change in stand age over time due to fires. This dataset shows conditions in 1920.

Fire perimeters 2000-2024. The national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the final fire perimeters datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, and CalFire. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies.2000-2023 fire perimeters were sourced from “InterAgencyFirePerimeterHistory All Years View” and 2024 fire perimeters were sourced from “WFIGS Interagency Fire Perimeters”, both of which are hosted on NIFC. This layer has been clipped to contain all fires that partially or completely occurred in Oregon and restricted to fires with a discovery date on or after 1/1/2000 for use in the SageCon Landscape Planning Tool on Oregon Explorer. QA/QC was performed to eliminate duplicate polygons based on incident names, however, some duplicate records may exist in the dataset because some fires had multiple incident names. The attributes table has been condensed to Incident name, polygon source, fire year, and GIS acres for simplicity.

This dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery. The collection totaled 3805 field plots with lidar imagery for 176 collections acquired between 2002 and 2016. Plot-level AGB estimates were calculated from tree measurements using the default allometric equations found in the Fire Fuels Extension (FFE) of the Forest Vegetation Simulator (FVS). The random forest algorithm was used to model AGB from lidar height and density metrics that were generated from the lidar returns within fixed-radius field plot footprints, gridded climate metrics obtained from the Climate-FVS Ready Data Server, and topographic estimates extracted from Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global elevation rasters. AGB was then mapped from the same lidar metrics gridded across the extent of the lidar collections at 30-m resolution. The standard deviation of estimated AGB of the terminal nodes from the random forest predictions was also mapped to show pixel-level model uncertainty. Note that the AGB estimates are, for the most part, a single snapshot in time and that the forest conditions are not necessarily representative of the larger study area.

(Summary adapted from on-line metadata description.)

 A series of four maps showing the state of forests in the northern coastal area
 of Oregon. They show the change in stand age over time due to fires. The maps
 show conditions in 1850, 1890, 1920, and 1940.

An All-Lands Explorer web map that combines layers relevant to Rogue Forest Partners (RFP) forest restoration project areas in the Rogue Basin Strategy (RBS) analysis area. Project areas in this context are large spatially defined operational areas meeting two criteria: (1) Contain units where treatments are ecological forestry aligned with the Rogue Basin Strategy, and/or (2) units are within our monitoring scope, with RFP monitoring plots and treatment tracking. Project areas include a continuous landscape of both treatment and non-treatment areas, typically include other past and current treatment units that are not aligned with the RBS or RFP, and may be pending, active, or completed in terms of work on the ground. This map focuses on project areas in the RBS to support understanding of this topic and relationships between key spatial data layers; it also provides a template for users to create new custom maps on this theme. This map was created by the Southern Oregon Forest Restoration Collaborative (SOFRC) using spatial data layers sourced from multiple agencies and organizations. The map is a component of the All-Lands Explorer Project Planning Atlas and is primarily intended for information and planning use by Rogue Forest Partners members and collaborators. The individual feature and image layers contained in this map are listed below and can be found in the All-Lands Explorer spatial data library. Click the (i) information icon for each layer to access descriptive information on the layer's source, content, use, and attribute definitions.

Biomass estimates for shrubland-dominated ecosystems in southern California have, to date, been limited to national or statewide efforts which can underestimate the amount of biomass; are limited to one-time snapshots; or estimate aboveground live biomass only. We developed a consistent, repeatable method to assess four vegetative biomass pools from 2001-2023 for our southern California study area (totaling 6,441,208 ha), defined by the Level IV Ecoregions (Bailey 2016) that intersect with USDA Forest Service lands (Figure 1). We first generated aboveground live biomass estimates (Schrader-Patton and Underwood 2021), and then calculated belowground, standing dead, and litter biomass pools using field data in the peer-reviewed literature (Schrader-Patton et al. 2022) (Figure 2). Over half (52.3%) of the study area is shrubland, and our method accounts for three post-fire shrub regeneration strategies: obligate resprouting, obligate seeding, and facultative seeding shrubs. We also generate biomass estimates for trees and herbs, giving a total of five life form/life history types. These data provide an important contribution to the management of shrubland-dominated ecosystems to assess the impacts of wildfire and management activities, such as fuel management and restoration, and for monitoring carbon storage over the long term. The biomass data are a key input into the online web mapping tool SoCal EcoServe, developed for US Department of Agriculture Forest Service resource managers to help evaluate and assess the impacts of wildfire on a suite of ecosystem services including carbon storage. The tool is available at https://manzanita.forestry.oregonstate.edu/ecoservices/ and described in Underwood et al. (2022). REFERENCES Bailey, R.G. 2016. Bailey's ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Forest Service Research Data Archive. (Fort Collins, Colorado). https://doi.org/10.2737/RDS-2016-0003 Schrader-Patton, C.C. and E.C. Underwood. 2021. New biomass estimates for chaparral-dominated southern California landscapes. Remote Sensing, 13, 1581. https://doi.org/10.3390/rs13081581 Schrader-Patton et al. 2022. “Estimating Wildfire Impacts on the Biomass of Southern California’s Chaparral Shrublands.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Underwood et al. 2022. “Estimating the Impacts of Wildfire on Chaparral Shrublands in Southern California using an Online Web Mapping Tool.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Methods METHODS We generated spatial estimates of above ground live biomass (AGLBM, in kg/m2) for 2000-2021 for our southern California study area. The study area, totaling 6,441,208 ha, is defined by the 42 Level IV Ecoregions (Bailey 2016) that intersect the four southern US Department of Agriculture (USDA) National Forests in southern California (Figure 1). We created biomass raster layers (30m spatial resolution) by modeling a set of covariates (Normalized Difference Vegetation Index [NDVI], precipitation, solar radiation, actual evapotranspiration, aspect, slope, climatic water deficit, elevation, flow accumulation, landscape facets, hydrological recharge and runoff, and soil type) to predict AGLBM using 766 field plots of biomass from the USDA Forest Service Forest Inventory and Analysis (FIA); the Landfire Reference Database (LFRDB) plot data; and other research plots. The dates of field data spanned 2001-2012. The NDVI raster data were derived from Landsat TM/ETM+/OLI multispectral satellite data (onboard Landsat 5, 7, and 8, respectively). NDVI data were composited from all available Landsat images for the months of July and August for each year 2001-2023. We also downloaded annual precipitation data for each water year (October 1 - September 30) 2001-2021 from PRISM (http://www.prism.oregonstate.edu/). For each field plot, we extracted the raster values for all covariates; NDVI and precipitation data were matched to the year of plot visit. We predicted AGLBM using the set of 17 covariates (Random Forest [RF] algorithm in R statistical computing software). To create an AGLBM raster surface for each year 2001-2023, we used NDVI and precipitation raster data specific to each year in the RF (using predict function in the R raster module) (see Schrader-Patton and Underwood 2021 for details). To estimate other shrubland biomass pools (standing dead, litter, and below ground) we employed a multi-step process: 1) First, we segregated the study area by community type using the California Wildlife Habitat Relationships (CWHR) data (Mayer and Laudenslayer 1988). The wildland vegetation of the study area (excluding agricultural, urban, water, and barren classes) contains 45 CWHR classes, 14 of which are >=0.75% of the wildland vegetation in the study area. We divided these 14 classes into shrubland dominated versus non-shrubland dominated types (annual grass, oak, conifer, mixed hardwood) (Table 1). Table 1. The Community types (WHR class) that are >= 0.75% of all wildland vegetation in the study area and their % area of the southern California ecoregion

Community type (WHR class)

Vegetation type

Percent of wildland vegetation in study area

Mixed Chaparral

Shrub

29.2

Annual Grassland

Annual grass

15.9

Desert Scrub

Shrub

12.7

Coastal Scrub

Shrub

12.5

Coastal Oak Woodland

Oak

6.4

Chamise-Redshank Chaparral

Shrub

6.0

Pinyon-Juniper

Conifer

2.5

Montane Hardwood

Mixed hardwood

2.3

Blue Oak Woodland

Oak

2.0

Sierran Mixed Conifer

Conifer

1.2

Juniper

Conifer

1.1

Montane Hardwood-Conifer

Mixed hardwood-conifer

1.1

Montane Chaparral

Shrub

1.0

Sagebrush

Shrub

0.9

2) Second, for the shrubland types we determined the per pixel proportion of biomass by three plant life forms: tree, shrub, and herb. We further subdivided the shrub life form into three life history classes based on shrub post-fire regeneration strategies: Obligate Resprouters (OR), obligate seeders (OS), and facultative seeders (FS), providing five plant types in total. We created rasters depicting the proportion of biomass in each of the five plant types by first calculating the proportion of biomass in each type for the plots used in Schrader-Patton and Underwood (2021). The plot data contained individual plant species, crown width and height measurements. Using these measurements, we estimated the biomass for each individual plant within the plot by applying published allometric equations (see Schrader-Patton and Underwood 2021 for details). The individual plants in the plots were classified into the five plant types and the proportion of biomass in each type were calculated for each plot. A multinomial model was used to relate these proportions to a suite of geophysical and remote sensing variables which, in turn, was applied to raster surfaces of these predictors. The final outputs were raster maps of the proportion of biomass by life form (tree, shrub, herb) and, for shrubs, the proportion of biomass by life history type (OR, OS, and FS) (Underwood et al. in review). 3) Third, we estimated the standing dead, litter, and below ground biomass pools by either applying fractions of AGLBM gleaned the available published literature or by using biomass estimates in existing spatial datasets. The specific method used was dependent on the percentage of the WHR class in the study area and the vegetation type (shrub or non-shrub) (Figure 2).
a) For shrubland types >= 0.75% of all wildland vegetation in the study area (Mixed Chaparral, Desert Scrub, Coastal Scrub, Chamise Redshank Chaparral, Montane Chaparral, and Sagebrush), we used the proportion of the five plant types as a basis for applying the AGLBM factors from the literature. For litter estimates, we applied AGLBM factor of 0.78 (derived from Bohlman et al. 2018) to Mixed chaparral, Chamise-Redshank Chaparral, and Coastal scrub WHR classes. These shrubland types also contained tree and herb biomass. We estimated the litter and standing dead biomass for these plant types by multiplying the plant type proportion by AGLBM (Tree and herb AGLBM), or by the North American Wildland Fuels Database (NAWFD, Pritchard et al. 2018) litter biomass (Tree and herb litter and standing dead biomass), or by literature-derived factors (Tree and herb belowground biomass). Sagebrush, Montane chaparral, and Desert scrub were assigned litter biomass from the NAWFD data as these WHR types had no litter estimates in the literature.
b) For non-shrubland types >= 0.75% all wildland vegetation in the study area (Coastal Oak Woodland, Pinyon-Juniper, Montane Hardwood, Blue Oak Woodland, Sierran Mixed Conifer, Juniper, and Montane Hardwood-Conifer), the snag and litter NAWFD biomass estimates were used for standing dead and litter estimates, respectively. For belowground biomass, we used AGLBM factors from the literature based on the gross vegetation type (Oak, Conifer, or Mixed) and amount of total per pixel AGLBM. For example, for Oak WHR types (Coastal Oak Woodland, Blue Oak Woodland) <= 7 kg/m2 we used an AGLBM factor of 0.46 (see Mokany et al. 2006 for breakdown by class breaks). c) For all the remaining WHR classes (each < 0.75% of all wildland vegetation in the study area) and Annual Grasslands, we used the NAWFD snag and litter estimates (standing dead and litter biomass), and the California Air Resources Board (CARB, Battles et al. 2014) for our belowground estimates. The above ground, litter, standing dead, and below ground biomass raster layers for each

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

This is a measure, expressed as a relative index (0-1), of suitability (or similarity) of forest structure and composition for Northern Spotted Owl (NSO) ( Strix occidentalis caurina ) nesting/roosting. The data were developed by the Regional Ecosystem Office for the Northwest Forest Plan monitoring program. These data were produced using machine learning software Maxent and trained/tested with nesting/roosting pair locations (1993). As illustrated in the diagram below, an index near zero indicates forest structure/composition dissimilar to where NSO pairs nest/roost. An index nearer to one indicates similar conditions. Raster values are as follows:

' ¢ -1 = no data (non-forested)

' ¢ 0' 10000 = relative suitability index from 0 to 1 (with a x10000 scalar applied). A value of 10,000 would indicate perfect habitat suitability of a pixel. Maximum value found (within the California portion of the range of NSO) is 8,628.

Annual forest vegetation structure and composition maps (30-m pixel resolution) for forest-capable lands from 1986 to 2023 were generated using the gradient nearest neighbor (GNN) imputation modeling and mapping methodology developed by Oregon State University Department of Forest Ecosystems and Society's Landscape Ecology, Modeling, Mapping, and Analysis program (LEMMA 2020). GNN is a multivariate, nonparametric modeling and mapping framework that inputs forest inventory plot data to individual map pixels based on Landsat surface reflectance and environmental similarity in the gradient space (Ohmann and Gregory 2002). The version of GNN used in this analysis was based on the composite Landsat images produced to map the forest disturbances above, matching plot measurements to Landsat image years (Bell et al. 2021).

Methodological changes, described in detail in the late-successional and old- growth monitoring report (<https://www.fs.usda.gov/r6/reo/monitoring/northern- spotted-owl.php>), improved the quality of GNN compared to previous monitoring reports. This included using a consistent type of forest inventory plot for imputations, the ensemble LandTrendr imagery described above, imagery stabilization, and bootstrapped approximations utilizing multiple neighbors (k = 7) with weighted means proportional to the probability that a bootstrap sample would result in that plot being the nearest neighbor for a pixel.

This subspecies is found in the Northern California Region.

With 204 GPS-collared mule deer, the Beulah-Malheur herd is one of the most extensively recorded mule deer herds in Oregon. Mule deer primarily winter along the Malheur River and the Stinkingwater Mountains, with some as far south as the Owyhee River. Winter ranges are covered by Artemisia tridentata (big sagebrush), grassland, and encroaching Juniperus occidentalis (western juniper). Although spatially dispersed, much of the Beulah-Malheur herd collectively migrates northwest to reach summer ranges across the upper elevations of the Malheur National Forest, Pedro Mountain, and Cottonwood Mountain. Primary summer range vegetation includes A. t. vaseyana (mountain big sagebrush), Pinus ponderosa (ponderosa pine), and western juniper with mixed-conifer forests and mountain shrub communities at higher elevations. In 2014, the Buzzard Complex fire burned approximately 398,596 acres (161,306 ha) between Riverside, Oregon and State Route 78, allowing Taeniatherum caput-medusae (medusahead) and other invasive annual grasses to proliferate in areas originally lacking perennial plant cover. Mule deer cross several major roadways during migration, including U.S. Highway 20, U.S. Route 26, and U.S. Route 395, while Interstate 84 is a complete barrier on the east. U.S. Highway 20 transects winter ranges for both migratory and resident mule deer and the section between mileposts 135 and 258 along the Malheur River accounted for an average of 179 mule deer-vehicle collisions each year from 2010 to 2022. The Burns-Paiute Tribe is working with the Oregon Department of Fish and Wildlife (ODFW) and Oregon Department of Transportation (ODOT) to identify wildlife passage solutions on U.S. Highway 20. These mapping layers show the location of the winter ranges for mule deer (Odocoileus hemionus) in the Beulah-Malheur population in Oregon. They were developed from 303 migration sequences collected from a sample size of 179 animals comprising GPS locations collected every 5-13 hours.

DATA OVERVIEW

Mapped attributes are

• above ground biomass, AGB;
• downed wood biomass, i.e., the sum of coarse and fine woody debris, DWB;
• canopy bulk density, CBD;
• canopy height,CH;
• canopy base height, CBH;
• and canopy fuel load, CFL.

Models were fit using auxiliary information that included lidar data from 20 acquisitions in Oregon and climate data. Measurements in plots of the Forest Inventory and Analysis program (FIA) were used to obtain plot-level ground observations for predictive modeling. Tree and transect measurements in FIA plots were respectively used to obtain plot-level values of AGB and DWB. To obtain plot-level values of CBD, CH, CBH and CFL, tree measurements in FIA plots were processed with FuelCalc. Plot level auxiliary variables were obtained intersecting the axiliary information layers with the FIA plots. Predictive models were random forest models in which a parametric component was added to model the error variance. The error variance was modeled as a power function of the predictive value and was used to produce uncertainty maps. A different model was fit for each variable and the resulting models were used to obtain maps of synthetic predictions for all areas covered by the 20 lidar acquisitions. The modeled error variance was used to generate uncertainty maps for the predictions of each response variable. Model accuracy was assessed globally (for the entire dataset) and separately for each one of the 20 lidar acquisitions included in the dataset.

Results from the accuracy assessment can be found in Appendix A and Appendix B of Mauro et al. (2021).

Each variable has two associated maps. These maps are named using the following convention where VARIABLE is the acronym for each variable (AGB, DWB, CBD, CH, CBH or CFL):

Predictions of forest attributes:

VARIABLE.tif

Standard deviation of modeled errors:

SD_VARIABLE.tif

### There are two additional rasters. The first one, year.tif is necessary to obtain the reference year for each lidar acquisition. The second one, forest_mask.tif provides a forest vs non-forest mask. Forested areas are coded as 1s and non-forested areas with no-datas. This mask is a resampled subset of the PALSAR JAXA 2014 ‘New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007-2010) - version 1’ from the Japan Aerospace Exploration Agency Earth Observation Research Center (www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm). Its reference year is 2009. Models to predict forest attributes were created using ground observations in forested areas. For many applications it is advisable to use the provided mask to excluded non-forested areas from analyses. This can be done, for example, multiplying the desired raster by the forest mask. Exceptions to this may occur in relatively open forested lands where the mask eliminates areas that actually sustain forest. In those areas, the use of an add-hoc forest mask might be more appropriate. ### Reference year: year.tif ### Forest mask: forest_mask.tif ###

UNITS:

For a given variable, both predictions and standard deviation of model errors have the same units. These units are:

• Variable (Abreviation): Units

• Above ground biomass (AGB): Mg/ha

• Downed wood biomass (DWB):Mg/ha

• Canopy bulk density (CBD): Kg/m3 (Kilogram per cubic meter)

• Canopy height (CH): m

• Canopy base height (CBH): m

  Canopy fuel load (CFL):Mg/ha

COORDINATE REFERENCE SYSTEM:

The reference system for all maps is EPSG 5070

USAGE

These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights.

Please include the following citation in any publication that uses these data:

Mauro, F., Hudak, A.T., Fekety, P.A., Frank, B., Temesgen, H., Bell, D.M., Gregory, M.J., McCarley, T.R., 2021. Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon. Remote Sensing 13. https://doi.org/10.3390/rs13020261

This dataset consists of polygon features representing forest lands defined as Community Forests (Frey et al 2024). The polygons were sourced from several areas, including the USGS Protected Areas Database of the United States (PAD-US) and from the individual governing organization of some of the Community Forests (CFs, hereafter). In some cases, the boundaries were heads-up digitized using state tax lot data and maps of the Community Forest. There is not a formal definition of CFs, rather they are loosely defined as forest lands that are under the local control of the adjacent communities. They may be governed by the local government (city, county), non-government organizations. There are an estimated 136 CFs in the US (Hajjar et al 2024). This data set is a subset of 18 of those CFs which are the subject of on-going research by USDA Forest Service, Oregon State University, and North Carolina State University scientists. Frey, Gregory E.; Hajjar, Reem; Charnley, Susan; McGinley, Kathleen; Schelhas, John; Tarr, Nathan A.; McCaskill, Lauren; Cubbage, Frederick W. 2024. “Community Forests” in the United States – How Do we Know One When we See One?. Society & Natural Resources. 37(8): 1240-1252. https://doi.org/10.1080/08941920.2024.2361413.Reem Hajjar, Kathleen McGinley, Susan Charnley, Gregory E Frey, Meredith Hovis, Frederick W Cubbage, John Schelhas, Kailey Kornhauser, Characterizing Community Forests in the United States, Journal of Forestry, Volume 122, Issue 3, May 2024, Pages 273–284, https://doi.org/10.1093/jofore/fvad054